Function point analysis using NESMA: simplifying the sizing without simplifying the size


This paper examines the trade-off between the utility of outputs from simplified functional sizing approaches, and the effort required by these sizing approaches, through a pilot study. The goal of this pilot study was to evaluate the quality of sizing output provided by NESMA’s simplified size estimation methods, adapt their general principles to enhance their accuracy and extent of relevance, and empirically validate such an adapted approach using commercial software projects. A dataset of 11 projects was sized using this adapted approach, and these results compared with those of the established Indicative, Estimated and Full NESMA method approaches. The performances of these adaptations were evaluated against the NESMA approaches in three ways: (1) effort to perform; (2) the accuracy of the total function counts produced; and (3) the accuracy of the profiles of the function counts for each of the base functional component types. The adapted approach outperformed the Indicative NESMA in terms of sizing accuracy and generally performed as well as the Estimated NESMA across both datasets, and required only ~ 50 % of the effort incurred by the Estimated NESMA. This adapted approach, applied to varying levels of information presented in commercial requirements documentation, overcame some of the limitations of simplified functional sizing methods by providing more than simply the simplified ‘indication’ of overall functional size. The provision and refinement of the more detailed function profile enable a greater degree of validation and utility for the size estimate.

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The research team gratefully acknowledges the access to staff and documentation, and participation of Equiniti-ICS in this project. In particular, the team expresses thanks to Charlie Tuxworth, Technical Director at Equiniti-ICS, for his commitment to this work. The authors gratefully acknowledge the feedback from reviewers, which provided a valuable contribution to the completion and presentation of our research study. The research team also gratefully acknowledges financial support from the Department for Employment and Learning.

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Correspondence to P. Morrow.



See Table 10.

Table 10 Sizing data developed from each sizing method

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Morrow, P., Wilkie, F.G. & McChesney, I.R. Function point analysis using NESMA: simplifying the sizing without simplifying the size. Software Qual J 22, 611–660 (2014).

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  • Software size estimation
  • Function point analysis
  • Simplified estimation
  • Commercial projects